Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations997
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory141.2 KiB
Average record size in memory145.0 B

Variable types

Text2
Categorical2
Numeric13
Boolean1

Alerts

Acousticness is highly overall correlated with EnergyHigh correlation
Danceability is highly overall correlated with ValenceHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Valence is highly overall correlated with DanceabilityHigh correlation
Time_Signature is highly imbalanced (87.1%)Imbalance
Key has 138 (13.8%) zerosZeros
Instrumentalness has 307 (30.8%) zerosZeros
Popularity has 12 (1.2%) zerosZeros

Reproduction

Analysis started2024-11-13 19:30:50.312513
Analysis finished2024-11-13 19:31:14.922682
Duration24.61 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Track
Text

Distinct972
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:15.520118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length57
Median length37
Mean length16.411234
Min length3

Characters and Unicode

Total characters16362
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique947 ?
Unique (%)95.0%

Sample

1st rowBabe
2nd rowThe Rose
3rd rowCars
4th rowMagic
5th rowWe Don’t Talk Anymore
ValueCountFrequency (%)
the 142
 
4.2%
you 114
 
3.4%
love 99
 
3.0%
i 76
 
2.3%
me 75
 
2.2%
of 59
 
1.8%
to 57
 
1.7%
on 57
 
1.7%
in 50
 
1.5%
a 46
 
1.4%
Other values (1036) 2570
76.8%
2024-11-13T16:31:16.437664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2348
 
14.4%
e 1540
 
9.4%
o 1102
 
6.7%
n 858
 
5.2%
a 750
 
4.6%
t 731
 
4.5%
i 703
 
4.3%
r 700
 
4.3%
h 484
 
3.0%
l 457
 
2.8%
Other values (67) 6689
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2348
 
14.4%
e 1540
 
9.4%
o 1102
 
6.7%
n 858
 
5.2%
a 750
 
4.6%
t 731
 
4.5%
i 703
 
4.3%
r 700
 
4.3%
h 484
 
3.0%
l 457
 
2.8%
Other values (67) 6689
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2348
 
14.4%
e 1540
 
9.4%
o 1102
 
6.7%
n 858
 
5.2%
a 750
 
4.6%
t 731
 
4.5%
i 703
 
4.3%
r 700
 
4.3%
h 484
 
3.0%
l 457
 
2.8%
Other values (67) 6689
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2348
 
14.4%
e 1540
 
9.4%
o 1102
 
6.7%
n 858
 
5.2%
a 750
 
4.6%
t 731
 
4.5%
i 703
 
4.3%
r 700
 
4.3%
h 484
 
3.0%
l 457
 
2.8%
Other values (67) 6689
40.9%

Artist
Text

Distinct475
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:16.860533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length68
Median length37
Mean length13.372116
Min length2

Characters and Unicode

Total characters13332
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)29.5%

Sample

1st rowStyx
2nd rowBette Midler
3rd rowGary Numan
4th rowOlivia Newton-John
5th rowCliff Richard
ValueCountFrequency (%)
the 122
 
5.3%
56
 
2.4%
and 53
 
2.3%
john 39
 
1.7%
michael 31
 
1.4%
jackson 27
 
1.2%
billy 21
 
0.9%
band 21
 
0.9%
kenny 19
 
0.8%
madonna 17
 
0.7%
Other values (733) 1887
82.3%
2024-11-13T16:31:17.494806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1296
 
9.7%
e 1284
 
9.6%
a 992
 
7.4%
n 988
 
7.4%
o 786
 
5.9%
i 779
 
5.8%
r 736
 
5.5%
l 576
 
4.3%
t 544
 
4.1%
s 500
 
3.8%
Other values (61) 4851
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1296
 
9.7%
e 1284
 
9.6%
a 992
 
7.4%
n 988
 
7.4%
o 786
 
5.9%
i 779
 
5.8%
r 736
 
5.5%
l 576
 
4.3%
t 544
 
4.1%
s 500
 
3.8%
Other values (61) 4851
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1296
 
9.7%
e 1284
 
9.6%
a 992
 
7.4%
n 988
 
7.4%
o 786
 
5.9%
i 779
 
5.8%
r 736
 
5.5%
l 576
 
4.3%
t 544
 
4.1%
s 500
 
3.8%
Other values (61) 4851
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1296
 
9.7%
e 1284
 
9.6%
a 992
 
7.4%
n 988
 
7.4%
o 786
 
5.9%
i 779
 
5.8%
r 736
 
5.5%
l 576
 
4.3%
t 544
 
4.1%
s 500
 
3.8%
Other values (61) 4851
36.4%

Time_Signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
4
960 
3
 
31
5
 
4
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters997
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Length

2024-11-13T16:31:17.655372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:31:17.774055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 960
96.3%
3 31
 
3.1%
5 4
 
0.4%
1 2
 
0.2%

Danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62646138
Minimum0.174
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:17.920663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.174
5-th percentile0.3522
Q10.534
median0.633
Q30.735
95-th percentile0.8632
Maximum0.988
Range0.814
Interquartile range (IQR)0.201

Descriptive statistics

Standard deviation0.15159323
Coefficient of variation (CV)0.24198336
Kurtosis-0.16705225
Mean0.62646138
Median Absolute Deviation (MAD)0.101
Skewness-0.31197626
Sum624.582
Variance0.022980508
MonotonicityNot monotonic
2024-11-13T16:31:18.112185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.587 9
 
0.9%
0.62 8
 
0.8%
0.744 7
 
0.7%
0.702 7
 
0.7%
0.728 7
 
0.7%
0.706 7
 
0.7%
0.759 6
 
0.6%
0.539 6
 
0.6%
0.776 6
 
0.6%
0.513 6
 
0.6%
Other values (457) 928
93.1%
ValueCountFrequency (%)
0.174 1
0.1%
0.177 1
0.1%
0.2 1
0.1%
0.209 2
0.2%
0.237 1
0.1%
0.244 2
0.2%
0.245 1
0.1%
0.251 1
0.1%
0.253 1
0.1%
0.254 1
0.1%
ValueCountFrequency (%)
0.988 1
 
0.1%
0.98 1
 
0.1%
0.974 1
 
0.1%
0.96 1
 
0.1%
0.952 1
 
0.1%
0.947 1
 
0.1%
0.946 3
0.3%
0.936 1
 
0.1%
0.932 3
0.3%
0.931 1
 
0.1%

Energy
Real number (ℝ)

HIGH CORRELATION 

Distinct546
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63346991
Minimum0.0183
Maximum0.994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:18.302679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0183
5-th percentile0.2796
Q10.489
median0.652
Q30.797
95-th percentile0.935
Maximum0.994
Range0.9757
Interquartile range (IQR)0.308

Descriptive statistics

Standard deviation0.20386084
Coefficient of variation (CV)0.32181614
Kurtosis-0.33786684
Mean0.63346991
Median Absolute Deviation (MAD)0.152
Skewness-0.44168973
Sum631.5695
Variance0.041559243
MonotonicityNot monotonic
2024-11-13T16:31:18.482161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.743 7
 
0.7%
0.686 7
 
0.7%
0.649 6
 
0.6%
0.583 6
 
0.6%
0.912 6
 
0.6%
0.703 6
 
0.6%
0.749 6
 
0.6%
0.44 6
 
0.6%
0.803 5
 
0.5%
0.726 5
 
0.5%
Other values (536) 937
94.0%
ValueCountFrequency (%)
0.0183 1
0.1%
0.0191 1
0.1%
0.0238 1
0.1%
0.0332 1
0.1%
0.0348 1
0.1%
0.0357 1
0.1%
0.0431 1
0.1%
0.05 1
0.1%
0.0544 1
0.1%
0.0842 1
0.1%
ValueCountFrequency (%)
0.994 1
0.1%
0.991 2
0.2%
0.989 1
0.1%
0.988 1
0.1%
0.987 1
0.1%
0.98 1
0.1%
0.975 1
0.1%
0.974 1
0.1%
0.973 1
0.1%
0.972 1
0.1%

Key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2296891
Minimum0
Maximum11
Zeros138
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:18.625809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6567593
Coefficient of variation (CV)0.69923073
Kurtosis-1.3404992
Mean5.2296891
Median Absolute Deviation (MAD)3
Skewness0.0060003753
Sum5214
Variance13.371889
MonotonicityNot monotonic
2024-11-13T16:31:18.760451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 138
13.8%
9 125
12.5%
7 102
10.2%
2 96
9.6%
1 92
9.2%
5 85
8.5%
11 82
8.2%
4 80
8.0%
10 60
6.0%
6 56
5.6%
Other values (2) 81
8.1%
ValueCountFrequency (%)
0 138
13.8%
1 92
9.2%
2 96
9.6%
3 28
 
2.8%
4 80
8.0%
5 85
8.5%
6 56
5.6%
7 102
10.2%
8 53
 
5.3%
9 125
12.5%
ValueCountFrequency (%)
11 82
8.2%
10 60
6.0%
9 125
12.5%
8 53
5.3%
7 102
10.2%
6 56
5.6%
5 85
8.5%
4 80
8.0%
3 28
 
2.8%
2 96
9.6%

Loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct927
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.8847242
Minimum-28.98
Maximum-1.496
Zeros0
Zeros (%)0.0%
Negative997
Negative (%)100.0%
Memory size15.6 KiB
2024-11-13T16:31:18.916001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-28.98
5-th percentile-15.1546
Q1-11.262
median-8.269
Q3-6.042
95-th percentile-3.9786
Maximum-1.496
Range27.484
Interquartile range (IQR)5.22

Descriptive statistics

Standard deviation3.8315626
Coefficient of variation (CV)-0.43125285
Kurtosis2.5076355
Mean-8.8847242
Median Absolute Deviation (MAD)2.542
Skewness-1.1077154
Sum-8858.07
Variance14.680872
MonotonicityNot monotonic
2024-11-13T16:31:19.090535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.484 3
 
0.3%
-4.92 3
 
0.3%
-8.053 2
 
0.2%
-8.865 2
 
0.2%
-5.321 2
 
0.2%
-14.808 2
 
0.2%
-13.045 2
 
0.2%
-5.756 2
 
0.2%
-5.665 2
 
0.2%
-13.239 2
 
0.2%
Other values (917) 975
97.8%
ValueCountFrequency (%)
-28.98 1
0.1%
-28.939 1
0.1%
-27.683 1
0.1%
-25.973 1
0.1%
-25.572 1
0.1%
-25.467 1
0.1%
-23.92 1
0.1%
-22.231 1
0.1%
-21.261 1
0.1%
-21.191 1
0.1%
ValueCountFrequency (%)
-1.496 1
0.1%
-2.209 1
0.1%
-2.238 1
0.1%
-2.433 1
0.1%
-2.685 1
0.1%
-2.771 1
0.1%
-2.785 1
0.1%
-2.879 1
0.1%
-2.939 2
0.2%
-2.947 1
0.1%

Mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
1
688 
0
309 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters997
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Length

2024-11-13T16:31:19.256091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T16:31:19.370785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Most occurring characters

ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 688
69.0%
0 309
31.0%

Speechiness
Real number (ℝ)

Distinct480
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057634905
Minimum0.0227
Maximum0.524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:19.517392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0227
5-th percentile0.02628
Q10.0317
median0.0393
Q30.0565
95-th percentile0.15
Maximum0.524
Range0.5013
Interquartile range (IQR)0.0248

Descriptive statistics

Standard deviation0.055767764
Coefficient of variation (CV)0.967604
Kurtosis18.78652
Mean0.057634905
Median Absolute Deviation (MAD)0.0099
Skewness3.9415826
Sum57.462
Variance0.0031100435
MonotonicityNot monotonic
2024-11-13T16:31:19.701898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0329 10
 
1.0%
0.0284 9
 
0.9%
0.0287 8
 
0.8%
0.0276 8
 
0.8%
0.0306 8
 
0.8%
0.0423 7
 
0.7%
0.0295 7
 
0.7%
0.0289 7
 
0.7%
0.0366 7
 
0.7%
0.0282 6
 
0.6%
Other values (470) 920
92.3%
ValueCountFrequency (%)
0.0227 1
 
0.1%
0.0232 1
 
0.1%
0.0233 1
 
0.1%
0.0237 1
 
0.1%
0.0239 3
0.3%
0.0241 1
 
0.1%
0.0242 1
 
0.1%
0.0243 1
 
0.1%
0.0244 2
0.2%
0.0245 1
 
0.1%
ValueCountFrequency (%)
0.524 1
0.1%
0.465 1
0.1%
0.432 1
0.1%
0.385 1
0.1%
0.383 1
0.1%
0.356 1
0.1%
0.34 2
0.2%
0.336 1
0.1%
0.333 1
0.1%
0.328 1
0.1%

Acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct726
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24425805
Minimum3.48 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:19.885407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.48 × 10-6
5-th percentile0.003456
Q10.0433
median0.155
Q30.385
95-th percentile0.7782
Maximum0.996
Range0.99599652
Interquartile range (IQR)0.3417

Descriptive statistics

Standard deviation0.24848622
Coefficient of variation (CV)1.0173103
Kurtosis0.41014797
Mean0.24425805
Median Absolute Deviation (MAD)0.1316
Skewness1.1475883
Sum243.52527
Variance0.0617454
MonotonicityNot monotonic
2024-11-13T16:31:20.072906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.238 7
 
0.7%
0.134 5
 
0.5%
0.186 5
 
0.5%
0.115 5
 
0.5%
0.225 5
 
0.5%
0.152 5
 
0.5%
0.111 4
 
0.4%
0.256 4
 
0.4%
0.41 4
 
0.4%
0.216 4
 
0.4%
Other values (716) 949
95.2%
ValueCountFrequency (%)
3.48 × 10-61
0.1%
1.83 × 10-51
0.1%
2.42 × 10-51
0.1%
3.81 × 10-51
0.1%
4.44 × 10-51
0.1%
5.18 × 10-51
0.1%
8.1 × 10-51
0.1%
0.000189 1
0.1%
0.000202 1
0.1%
0.000205 1
0.1%
ValueCountFrequency (%)
0.996 1
0.1%
0.995 2
0.2%
0.993 1
0.1%
0.991 2
0.2%
0.982 1
0.1%
0.975 1
0.1%
0.962 1
0.1%
0.953 1
0.1%
0.949 1
0.1%
0.94 1
0.1%

Instrumentalness
Real number (ℝ)

ZEROS 

Distinct605
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.042586031
Minimum0
Maximum0.974
Zeros307
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:20.255453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.21 × 10-5
Q30.00138
95-th percentile0.312
Maximum0.974
Range0.974
Interquartile range (IQR)0.00138

Descriptive statistics

Standard deviation0.15713266
Coefficient of variation (CV)3.68977
Kurtosis19.676441
Mean0.042586031
Median Absolute Deviation (MAD)2.21 × 10-5
Skewness4.4561609
Sum42.458273
Variance0.024690672
MonotonicityNot monotonic
2024-11-13T16:31:20.439957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 307
30.8%
1.98 × 10-63
 
0.3%
0.0287 3
 
0.3%
5.68 × 10-53
 
0.3%
0.00126 3
 
0.3%
8.65 × 10-53
 
0.3%
0.000163 3
 
0.3%
0.00105 3
 
0.3%
1.16 × 10-63
 
0.3%
0.000276 3
 
0.3%
Other values (595) 663
66.5%
ValueCountFrequency (%)
0 307
30.8%
1.08 × 10-61
 
0.1%
1.09 × 10-62
 
0.2%
1.11 × 10-61
 
0.1%
1.12 × 10-61
 
0.1%
1.16 × 10-63
 
0.3%
1.22 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.28 × 10-61
 
0.1%
1.29 × 10-61
 
0.1%
ValueCountFrequency (%)
0.974 1
0.1%
0.964 1
0.1%
0.953 1
0.1%
0.939 1
0.1%
0.931 1
0.1%
0.929 1
0.1%
0.907 1
0.1%
0.905 1
0.1%
0.903 1
0.1%
0.898 1
0.1%

Liveness
Real number (ℝ)

Distinct571
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17866931
Minimum0.0223
Maximum0.981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:20.681280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0223
5-th percentile0.04662
Q10.0839
median0.113
Q30.226
95-th percentile0.5182
Maximum0.981
Range0.9587
Interquartile range (IQR)0.1421

Descriptive statistics

Standard deviation0.1627217
Coefficient of variation (CV)0.9107423
Kurtosis7.0109097
Mean0.17866931
Median Absolute Deviation (MAD)0.0446
Skewness2.4499773
Sum178.1333
Variance0.02647835
MonotonicityNot monotonic
2024-11-13T16:31:20.874762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 21
 
2.1%
0.105 14
 
1.4%
0.107 13
 
1.3%
0.112 11
 
1.1%
0.104 10
 
1.0%
0.108 10
 
1.0%
0.132 9
 
0.9%
0.134 8
 
0.8%
0.113 8
 
0.8%
0.117 7
 
0.7%
Other values (561) 886
88.9%
ValueCountFrequency (%)
0.0223 1
0.1%
0.0226 1
0.1%
0.0242 1
0.1%
0.0248 2
0.2%
0.0257 1
0.1%
0.0295 1
0.1%
0.0298 1
0.1%
0.03 1
0.1%
0.0307 1
0.1%
0.0311 1
0.1%
ValueCountFrequency (%)
0.981 1
0.1%
0.969 1
0.1%
0.965 1
0.1%
0.964 1
0.1%
0.952 1
0.1%
0.946 1
0.1%
0.936 1
0.1%
0.931 1
0.1%
0.926 2
0.2%
0.92 1
0.1%

Valence
Real number (ℝ)

HIGH CORRELATION 

Distinct589
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60297262
Minimum0.0287
Maximum0.984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:21.061262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0287
5-th percentile0.158
Q10.388
median0.644
Q30.825
95-th percentile0.962
Maximum0.984
Range0.9553
Interquartile range (IQR)0.437

Descriptive statistics

Standard deviation0.25797298
Coefficient of variation (CV)0.42783531
Kurtosis-1.0481126
Mean0.60297262
Median Absolute Deviation (MAD)0.211
Skewness-0.33272225
Sum601.1637
Variance0.066550058
MonotonicityNot monotonic
2024-11-13T16:31:21.253747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 12
 
1.2%
0.964 7
 
0.7%
0.961 7
 
0.7%
0.971 6
 
0.6%
0.746 6
 
0.6%
0.852 6
 
0.6%
0.969 5
 
0.5%
0.673 5
 
0.5%
0.888 5
 
0.5%
0.713 5
 
0.5%
Other values (579) 933
93.6%
ValueCountFrequency (%)
0.0287 1
0.1%
0.0384 1
0.1%
0.0395 1
0.1%
0.0396 1
0.1%
0.04 1
0.1%
0.0467 1
0.1%
0.0608 1
0.1%
0.0617 1
0.1%
0.0632 1
0.1%
0.0643 1
0.1%
ValueCountFrequency (%)
0.984 1
 
0.1%
0.982 1
 
0.1%
0.978 2
 
0.2%
0.975 2
 
0.2%
0.974 1
 
0.1%
0.972 2
 
0.2%
0.971 6
0.6%
0.97 2
 
0.2%
0.969 5
0.5%
0.968 1
 
0.1%

Tempo
Real number (ℝ)

Distinct955
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.9384
Minimum61.53
Maximum208.571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:21.421299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum61.53
5-th percentile80.4146
Q1102.477
median119.972
Q3135.003
95-th percentile170.531
Maximum208.571
Range147.041
Interquartile range (IQR)32.526

Descriptive statistics

Standard deviation26.245862
Coefficient of variation (CV)0.21701843
Kurtosis0.53357737
Mean120.9384
Median Absolute Deviation (MAD)16.33
Skewness0.58836025
Sum120575.59
Variance688.84529
MonotonicityNot monotonic
2024-11-13T16:31:21.598825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204.113 3
 
0.3%
146.967 2
 
0.2%
135.003 2
 
0.2%
73.36 2
 
0.2%
95.007 2
 
0.2%
140.034 2
 
0.2%
100.002 2
 
0.2%
141.283 2
 
0.2%
128.993 2
 
0.2%
151.566 2
 
0.2%
Other values (945) 976
97.9%
ValueCountFrequency (%)
61.53 1
0.1%
64.572 1
0.1%
71.111 1
0.1%
71.706 1
0.1%
72.17 1
0.1%
72.258 1
0.1%
72.795 1
0.1%
72.841 1
0.1%
73.012 1
0.1%
73.286 1
0.1%
ValueCountFrequency (%)
208.571 2
0.2%
205.726 1
 
0.1%
204.113 3
0.3%
203.989 1
 
0.1%
203.753 1
 
0.1%
202.408 1
 
0.1%
201.977 1
 
0.1%
201.952 1
 
0.1%
201.327 2
0.2%
200.733 1
 
0.1%

Popularity
Real number (ℝ)

ZEROS 

Distinct90
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.758275
Minimum0
Maximum96
Zeros12
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:21.790344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q149
median60
Q370
95-th percentile81.2
Maximum96
Range96
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.399648
Coefficient of variation (CV)0.30124943
Kurtosis1.4384764
Mean57.758275
Median Absolute Deviation (MAD)10
Skewness-0.97970241
Sum57585
Variance302.74774
MonotonicityNot monotonic
2024-11-13T16:31:21.973821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 35
 
3.5%
67 31
 
3.1%
58 30
 
3.0%
65 28
 
2.8%
56 27
 
2.7%
61 27
 
2.7%
75 27
 
2.7%
55 26
 
2.6%
53 26
 
2.6%
54 26
 
2.6%
Other values (80) 714
71.6%
ValueCountFrequency (%)
0 12
1.2%
1 3
 
0.3%
3 2
 
0.2%
4 2
 
0.2%
5 1
 
0.1%
6 2
 
0.2%
7 3
 
0.3%
8 1
 
0.1%
9 1
 
0.1%
10 2
 
0.2%
ValueCountFrequency (%)
96 1
 
0.1%
93 1
 
0.1%
92 2
 
0.2%
91 2
 
0.2%
90 1
 
0.1%
89 1
 
0.1%
88 4
0.4%
87 1
 
0.1%
86 4
0.4%
85 9
0.9%

Year
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.4985
Minimum1980
Maximum1989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:22.123420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1980
Q11982
median1984
Q31987
95-th percentile1989
Maximum1989
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8747704
Coefficient of variation (CV)0.0014486131
Kurtosis-1.2243983
Mean1984.4985
Median Absolute Deviation (MAD)3
Skewness0.00041401127
Sum1978545
Variance8.264305
MonotonicityIncreasing
2024-11-13T16:31:22.270029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1980 100
10.0%
1981 100
10.0%
1983 100
10.0%
1984 100
10.0%
1987 100
10.0%
1986 100
10.0%
1989 100
10.0%
1982 99
9.9%
1985 99
9.9%
1988 99
9.9%
ValueCountFrequency (%)
1980 100
10.0%
1981 100
10.0%
1982 99
9.9%
1983 100
10.0%
1984 100
10.0%
1985 99
9.9%
1986 100
10.0%
1987 100
10.0%
1988 99
9.9%
1989 100
10.0%
ValueCountFrequency (%)
1989 100
10.0%
1988 99
9.9%
1987 100
10.0%
1986 100
10.0%
1985 99
9.9%
1984 100
10.0%
1983 100
10.0%
1982 99
9.9%
1981 100
10.0%
1980 100
10.0%

Featuring
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
887 
True
110 
ValueCountFrequency (%)
False 887
89.0%
True 110
 
11.0%
2024-11-13T16:31:22.387714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Duration_seconds
Real number (ℝ)

Distinct249
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.21866
Minimum41
Maximum929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-11-13T16:31:22.527340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile152.8
Q1205
median234
Q3270
95-th percentile339
Maximum929
Range888
Interquartile range (IQR)65

Descriptive statistics

Standard deviation63.703192
Coefficient of variation (CV)0.26518836
Kurtosis17.891376
Mean240.21866
Median Absolute Deviation (MAD)33
Skewness2.1023678
Sum239498
Variance4058.0967
MonotonicityNot monotonic
2024-11-13T16:31:22.723816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221 14
 
1.4%
249 13
 
1.3%
229 13
 
1.3%
234 12
 
1.2%
258 11
 
1.1%
256 11
 
1.1%
220 11
 
1.1%
238 11
 
1.1%
222 11
 
1.1%
209 10
 
1.0%
Other values (239) 880
88.3%
ValueCountFrequency (%)
41 1
0.1%
59 2
0.2%
72 1
0.1%
76 1
0.1%
79 1
0.1%
89 1
0.1%
101 1
0.1%
104 1
0.1%
110 2
0.2%
111 1
0.1%
ValueCountFrequency (%)
929 1
0.1%
738 1
0.1%
543 1
0.1%
506 1
0.1%
484 1
0.1%
478 1
0.1%
438 1
0.1%
434 1
0.1%
423 1
0.1%
415 1
0.1%

Interactions

2024-11-13T16:31:12.756509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:51.088437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.159898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.177499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.820106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.475711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.234971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.216672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.851297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.552773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.171417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.976619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.702972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.891116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:51.275938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.294538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.326102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.967711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.618296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.376593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.360286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.986935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.693371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.320020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.117248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.841600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.013815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:51.676863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.418238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.450768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.090383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.786845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.535169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.490936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.110605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.818070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.446680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.246866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.961279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.133501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:51.819481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.544868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.567456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.212058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.950408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.694742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.612636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.228290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.933727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.572344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.368541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:11.077967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.260129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:51.952161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.670531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.689131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.336725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.079064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.821403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.745256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.349964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.057398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.703028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.491212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:11.293392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.378812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.093750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.800186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.817786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.466378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.203731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.952054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.868926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.472667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.192037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.894483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.616875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:11.418089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.502482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.235369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.924851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.941491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.589048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.332390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.073746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:02.990600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.596306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.309722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.023139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.745563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:11.848905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.622197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.368014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:54.418530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.077127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.717705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.456054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.202384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.111277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.717014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.434388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.150797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:09.877179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:11.969582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.743836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.499664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:54.541201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.199765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.844366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.581730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.322063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.233949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.838657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.552073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.278480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.002843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.094284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.869499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.630349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:54.671852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.327424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:57.969032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.708380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.445733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.355623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:04.960332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.672752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.406149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.126512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.276760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:13.996161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.767945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:54.806493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.458075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.105667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.849011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.577381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.488269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.090982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.811381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.599597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.258161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.403422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:14.122822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:52.901587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:54.930161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.577754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.229338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:59.975666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.710026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.609943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.306406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:06.933055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.723300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.376842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.527091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:14.242502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:53.029245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:55.050840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:56.694441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:30:58.355000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:00.111302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:01.831702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:03.729623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:05.432070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:07.055726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:08.850924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:10.535450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T16:31:12.640787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-13T16:31:22.854465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcousticnessDanceabilityDuration_secondsEnergyFeaturingInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime_SignatureValenceYear
Acousticness1.000-0.181-0.108-0.5170.059-0.059-0.0220.023-0.3460.074-0.116-0.233-0.0690.132-0.244-0.165
Danceability-0.1811.0000.0450.0850.0000.1390.013-0.255-0.0170.1440.0300.210-0.1340.1290.543-0.053
Duration_seconds-0.1080.0451.0000.0060.0000.1600.034-0.099-0.1700.000-0.102-0.135-0.0330.0500.0360.134
Energy-0.5170.0850.0061.0000.0350.0550.0340.1340.6730.0240.0660.2780.1430.1250.3980.136
Featuring0.0590.0000.0000.0351.0000.0710.0440.0000.0910.0500.0350.0000.0000.0180.0000.000
Instrumentalness-0.0590.1390.1600.0550.0711.0000.045-0.109-0.1840.000-0.144-0.0390.0050.1810.1180.001
Key-0.0220.0130.0340.0340.0440.0451.0000.0300.0020.1930.0230.0790.0000.0720.0060.008
Liveness0.023-0.255-0.0990.1340.000-0.1090.0301.0000.1650.000-0.0270.0570.0100.039-0.1370.005
Loudness-0.346-0.017-0.1700.6730.091-0.1840.0020.1651.0000.0000.1980.1800.0720.0410.0430.068
Mode0.0740.1440.0000.0240.0500.0000.1930.0000.0001.0000.0000.1330.0420.0000.0860.073
Popularity-0.1160.030-0.1020.0660.035-0.1440.023-0.0270.1980.0001.0000.028-0.0170.036-0.031-0.002
Speechiness-0.2330.210-0.1350.2780.000-0.0390.0790.0570.1800.1330.0281.0000.0830.1480.142-0.027
Tempo-0.069-0.134-0.0330.1430.0000.0050.0000.0100.0720.042-0.0170.0831.0000.0220.028-0.011
Time_Signature0.1320.1290.0500.1250.0180.1810.0720.0390.0410.0000.0360.1480.0221.0000.1120.000
Valence-0.2440.5430.0360.3980.0000.1180.006-0.1370.0430.086-0.0310.1420.0280.1121.000-0.063
Year-0.165-0.0530.1340.1360.0000.0010.0080.0050.0680.073-0.002-0.027-0.0110.000-0.0631.000

Missing values

2024-11-13T16:31:14.427041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-13T16:31:14.756160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TrackArtistTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearFeaturingDuration_seconds
0BabeStyx40.7000.58211-5.96000.03560.050200.0000000.08810.785116.712961980False218
1The RoseBette Midler40.2640.6408-6.22110.04420.039300.0000020.15100.19084.828921980False244
2CarsGary Numan40.3380.5629-7.18110.02900.039000.0000000.10700.259149.907821980False248
3MagicOlivia Newton-John40.9110.6891-6.17610.26500.001190.0000000.07040.546140.034801980False137
4We Don’t Talk AnymoreCliff Richard40.7280.5631-8.05300.13400.621000.0000000.17900.352100.017801980False217
5DesireAndy Gibb40.5870.92411-5.43300.04570.031100.0171000.23000.509140.009791980False179
6Real LoveDoobie Brothers40.4170.6867-6.48410.03730.095500.0287000.09890.625204.113791980False189
7Rock With YouMichael Jackson40.8080.5351-12.52110.03530.179000.0000990.15800.848114.031791980False220
8SaraFleetwood Mac40.5760.6837-5.10310.11800.410000.0000000.08190.433151.566771980False214
9Upside DownDiana Ross40.7920.6474-8.31410.04500.255000.0002230.13200.694102.477771980False209
TrackArtistTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearFeaturingDuration_seconds
988Secret RendezvousKaryn White40.7330.68910-13.00100.03880.0503000.0011500.06560.888111.695371989False337
989Girl I’m Gonna Miss YouMilli Vanilli40.7320.5669-7.76010.05050.1310000.0000420.18400.76375.692361989False264
990Two HeartsPhil Collins40.2000.9640-3.69110.11600.0005040.0000020.93600.231147.324311989False194
991I’ll Be There For YouBon Jovi40.5160.53911-6.45300.08960.2340000.0000920.11400.311123.936301989False263
992The Lover In MeSheena Easton40.8180.8087-8.53600.04670.0274000.0000000.05920.931115.221281989False301
993When I Looked At HimExposé40.4550.7138-10.95910.04230.2300000.0003320.37000.469169.633241989False259
994I Remember Holding YouBoys Club40.6090.8432-7.97300.04470.3070000.0005980.05170.76292.480191989False293
995Don’t Rush MeTaylor Dayne40.6860.8477-7.69210.28400.4450000.0000000.25000.74890.793151989False272
996I’ll Be Loving You (FOREVER)New Kids on the Block40.3710.71011-6.43710.04810.0964000.0000000.74000.223135.003151989False364
997Baby, I Love Your Way/Freebird MedleyWill to Power40.5810.4987-9.24610.03380.2390000.7830000.05160.601152.45001989False245